Another DBrief from Deloitte on The Performance Imperative: Reaching Beyond Cost Cuts and Product Delivery Improvements.

Great slide that I think relates to radiology and improving processes and the business. Often we make a list of 8 projects and we know they all need to be done, so we do them all at once. We need to improve referrals, RVU productivity of reading radiologist and modify workflow to increase productivity- among others. Instead of trying to tackle all of them at once, split them up and complete them one at a time, as the graph above demonstrates.

A company’s brand can can either catapult them to success or put them on the fast track to failure. In this talk from Chicago Founder Institute mentor, Joe Bezdek, Joe advises founders on brand development, providing them with tips and tricks to establishing an emotional connection with customers. Afterall, its not about being the biggest and the best - its about providing a positive and memorable experience.Joe Bezdek is a co-founder of DivX, Inc., the creator of the popular DivX video format and technology. While at DivX, he filled a variety of roles in product management, marketing, and brand management, most recently as brand director.Joe concludes his talk with some lessons in naming; he says that a company’s name is a vessel for the brand essence. A good vessel should reinforce the emotional connection and experience customers have with the product.Clicking on the video below will open a link to the FI page and the video

In Nate Silver's book "The Signal and the Noise"- are included a couple of interesting graphs. These graphs demonstrate very well the difference between noise and signal and the difficulty of prediction. The image above are line graphs of the hitting performance of professional baseball hitters. Each line is a different hitter.

Below is the graph for the "signal" the aggregate trends. What do you see in your life or profession that may resemble the graph above, but understand the graph below would help?

They should be passionate believers in analytical and fact based decision making. You cannot inspire others to change their behavior in a more analytical direction if oure not passionate about the goal.

They should have some appreciation of analytical tools and methods. They need not be analytical experts, but they should have an understanding of the tools and qualitative assumptions that are a part of the analytics.

They should be willing to act on the results of the analyses. The "action" stage is the most important piece of analytics. There is no point in commissioning detailed analytics if nothing different will be done based on the outcome.

They should be willing to manage a meritocracy. It will become apparent who is performing and wh isn't. Those that are performing should be rewarded; those who don't perform shouldn't be string along for long periods. Nothing good comes from inaction when performance differences are visible.

My Thoughts: This chapter is not very well done. It's focus is on the executives that manage analytical talent and not actually managing the analytical talent. One of the best books I have ever read is "Leading Geeks: How to manage and lead people who deliver technolgy" -- I don't expect this level of treatment in this book, but I do expect something better than analytics needs executive support- and then once executive support is there, that analytics will magically work well. It won't- most of the examples from the book are from organizations that are lead by people with an analytical background. The reader would be better served with an analysis of what these leaders did to encourage their talent.

How does the initiative improve our enterprise-wide analytical capabilities?

What complementary change need to be made in order to take full advantage of new capabilities, such as developing new or enhanced skills; improving IT, training, and processes; or redesigning jobs?

Does the right data exist? If not, can we get it? Is the data timely, consisten, accurate, and complete?

Is the technology reliable? Is it cost-effective? Is it scalable? Is this the right approach or tool for the right job?

Some missteps are due primarily to ignorance. Most common errors of omission are:

Focusing excessively on one dimension of analytical capability (e.g. too much technology)

Attempting to do everything at once

Investing excessive resources on analytics that have minimal impact on the business

INvesting too much or too little in any analytical capability, compared with demand

Choosing the wrong problem, not understanding the problem sufficiently, using eh wrong analytical technique or the wrong analytical software

Automating decision-based applications without carefully monitoring outcomes and external conditions to see whether assumptions need to be modified

My Thoughts:There is here an element of risk. What will be the expected outcome based on the implementation of analytics? What does the upside look like and what is the downside? Analytics should be looked at like any other investment. What is the return? What costs can be reduced? How much more revenue can we increase? At the heart are always these assumptions. Other considerations are important, too. How much faster could we move? Can we increase throughput? Are our clients demanding better analytics with our product or service?

The costs of delivering analytics has dropped a lot in the past 5 years. Better software and cheaper software. It has become crucial pieces of some industries- while others are just starting to wonder what could be...

This is a chapter review of "The Signal and the Noise. Why so many predictions fail- but some don't" by Nate Silver

The invention of the printing press changed the trajectory of mankind. Recording information in books was costly, error prone and subject to decay and loss. Everything would soon be forgotten. Books allowed us to store and share information and grow at an exponential rate.

With all of this data that we have now- so has the expectation that we will be able to predict the future. "The signal is the truth. The noise is what distracts us from the truth. this is a book about the signal and the noise."

My Thoughts:I appreciate the approach to truth and the distinction to noise, or not truth. I just received this book- today. So, looking forward to reading and posting my thoughts on this blog.

Analytical competitors look beyond basic statistics and do the following:

They use predictive modeling to identify the most profitable customers- as well as those with the greatest profit potential and the ones most likely to cancel their accounts.

They integrate data generated in-house with data acquired from outside sources for a comprehensive understanding of their customers

They optimize their supply chains and can this determine the impact of unexpected glitches, simulate alternatives, and route shipments around problems

They analyze historical sales and pricing trends to establish prices in real time and get the highest yield possible from each transaction.

They use sophisticated experiments to measure the overall impact or "lift" of advertising and other marketing strategies and then apply their insights to future analyses.

Typical Analytical Applications in Marketing

CHAID- h-square automatic interaction detection. Used to segment customers on the basis of alternative variables.

Conjoint analysis- Typically used to evaluate the strength and direction of customer preferences for a combination of product or service attributes. Determine which which factors (price, quality, location...) are most important.

Lifetime value analysis- Analytical model to assess the profitability of an individual customer over a lifetime of transactions. Other models generate estimates of costs incurred by the customer in purchasing and expenses from call centers.

Market experiments- Using direct mail, changes in the Web site, promotions, and other techniques, marketers test variables to determine what customers respond to most in a given offering.

Multiple regression analysis- Most common statistical technique for predicting the value of a dependent variable (sales) in relation to one or more independent variables (number of salespeople, temperature, day of the month). While basic regression assumes linear relationships, modification of the modela can deal with non-linearity, logarithmic relationships etc...

Price optimization- Also known as yield or revenue management, this technique assumes the primary causal variable in customer purchase behavior is price. Key issue is usually price elasticity.

Time series experiments- Experimental designs to follow a particular population for successive points in time. Could be used to determine the impact of exposure to advertising.